mirror of
https://github.com/blakeblackshear/frigate.git
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97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
import logging
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import numpy as np
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from typing import Literal
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from pydantic import Extra, Field
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import tflite_runtime.interpreter as tflite
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from tflite_runtime.interpreter import load_delegate
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "edgetpu"
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class EdgeTpuDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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device: str = Field(default=None, title="Device Type")
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class EdgeTpuTfl(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: EdgeTpuDetectorConfig):
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self.is_audio = detector_config.model.type == "audio"
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device_config = {"device": "usb"}
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if detector_config.device is not None:
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device_config = {"device": detector_config.device}
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edge_tpu_delegate = None
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try:
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logger.info(f"Attempting to load TPU as {device_config['device']}")
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edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
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logger.info("TPU found")
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default_model = (
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"/edgetpu_model.tflite"
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if not self.is_audio
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else "/edgetpu_audio_model.tflite"
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)
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self.interpreter = tflite.Interpreter(
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model_path=detector_config.model.path or default_model,
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experimental_delegates=[edge_tpu_delegate],
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)
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except ValueError:
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logger.error(
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"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
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)
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raise
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self.interpreter.allocate_tensors()
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self.tensor_input_details = self.interpreter.get_input_details()
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self.tensor_output_details = self.interpreter.get_output_details()
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def detect_raw(self, tensor_input):
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self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
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self.interpreter.invoke()
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detections = np.zeros((20, 6), np.float32)
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if self.is_audio:
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res = self.interpreter.get_tensor(self.tensor_output_details[0]["index"])[0]
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non_zero_indices = res > 0
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class_ids = np.argpartition(-res, 20)[:20]
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class_ids = class_ids[np.argsort(-res[class_ids])]
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class_ids = class_ids[non_zero_indices[class_ids]]
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scores = res[class_ids]
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boxes = np.full((scores.shape[0], 4), -1, np.float32)
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count = len(scores)
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else:
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boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
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class_ids = self.interpreter.tensor(
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self.tensor_output_details[1]["index"]
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)()[0]
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scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[
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0
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]
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count = int(
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self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
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)
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for i in range(count):
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if scores[i] < 0.4 or i == 20:
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break
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detections[i] = [
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class_ids[i],
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float(scores[i]),
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boxes[i][0],
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boxes[i][1],
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boxes[i][2],
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boxes[i][3],
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]
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return detections
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